feat: DAG for prediction

dag_prediction.py

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+from datetime import datetime, timedelta
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow.models.dag import DAG
+
+# Operators; we need this to operate!
+from airflow.operators.bash import BashOperator
+
+date = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+with DAG(
+    "get-annual-report-pdf",
+    # These args will get passed on to each operator
+    # You can override them on a per-task basis during operator initialization
+    default_args={
+        "depends_on_past": False,
+        "email": ["airflow@example.com"],
+        "email_on_failure": False,
+        "email_on_retry": False,
+        "retries": 1,
+        "retry_delay": timedelta(minutes=5),
+    },
+    description="A test DAG",
+    schedule="5 9 1 * *",
+    start_date=datetime(2024, 3, 6),
+    catchup=False,
+    tags=["Daily"],
+) as dag:
+
+    # t1, t2 and t3 are examples of tasks created by instantiating operators
+
+    t6 = BashOperator(
+        task_id="make-target",
+        depends_on_past=False,
+        bash_command="python3.9 make_targets_yf.py --sqlite price.sqlite",
+    )
+
+    t7 = BashOperator(
+        task_id="prediction",
+        depends_on_past=False,
+        bash_command="python3.9 predict_top5.py ",
+    )
+    t6 >> t7